
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from ipywidgets import interact


import matplotlib.pyplot as plt


data=pd.read_csv('owid-covid-data.csv')


data.head()


columns={'Albania','Armenia','Azerbijan','Bangladesh','Benin','Brazil','Cambodia','Chile','China','Colombia','Dominican republic','Ecuador','Egypt','El Salvador','Georgia',
'Hong kong','Indonesia','Kazakhstan','Democratic Republic','Malaysia','Mexico,Nepal','Oman','Pakistan','Panama','Paraguay','Philippines','South Africa','Sri Lanka',
'Tajikistan','Thailand','Timor-Leste','Togo,Tunisia','Turkey','Ukraine','United Republic of Tanzania','Uruguay','zimbabwe'
}


column1=set(data.location)


cols_to_consider=column1.intersection(columns)


cols_to_consider

{'Albania',
 'Armenia',
 'Bangladesh',
 'Benin',
 'Brazil',
 'Cambodia',
 'Chile',
 'China',
 'Colombia',
 'Ecuador',
 'Egypt',
 'El Salvador',
 'Georgia',
 'Indonesia',
 'Kazakhstan',
 'Malaysia',
 'Oman',
 'Pakistan',
 'Panama',
 'Paraguay',
 'Philippines',
 'South Africa',
 'Sri Lanka',
 'Tajikistan',
 'Thailand',
 'Turkey',
 'Ukraine',
 'Uruguay'}


new_data=data[data.location.isin(cols_to_consider)]
new_data


new_data.shape

(15103, 62)


new_data.location.nunique()

28


after_vaccination=new_data[new_data.total_vaccinations.isnull()==False]


after_vaccination.head()


after_vaccination.shape

(3545, 62)


after_vaccination.isnull().sum()[1:61]

continent                                   0
location                                    0
date                                        0
total_cases                                 0
new_cases                                   0
new_cases_smoothed                          0
total_deaths                               25
new_deaths                                 26
new_deaths_smoothed                         0
total_cases_per_million                     0
new_cases_per_million                       0
new_cases_smoothed_per_million              0
total_deaths_per_million                   25
new_deaths_per_million                     26
new_deaths_smoothed_per_million             0
reproduction_rate                          39
icu_patients                             3545
icu_patients_per_million                 3545
hosp_patients                            3545
hosp_patients_per_million                3545
weekly_icu_admissions                    3545
weekly_icu_admissions_per_million        3545
weekly_hosp_admissions                   3545
weekly_hosp_admissions_per_million       3545
new_tests                                1167
total_tests                              1070
total_tests_per_thousand                 1070
new_tests_per_thousand                   1167
new_tests_smoothed                        889
new_tests_smoothed_per_thousand           889
positive_rate                             993
tests_per_case                            993
tests_units                               880
total_vaccinations                          0
people_vaccinated                         310
people_fully_vaccinated                   716
total_boosters                           3512
new_vaccinations                          549
new_vaccinations_smoothed                  28
total_vaccinations_per_hundred              0
people_vaccinated_per_hundred             310
people_fully_vaccinated_per_hundred       716
total_boosters_per_hundred               3512
new_vaccinations_smoothed_per_million      28
stringency_index                          280
population                                  0
population_density                          0
median_age                                  0
aged_65_older                               0
aged_70_older                               0
gdp_per_capita                              0
extreme_poverty                           353
cardiovasc_death_rate                       0
diabetes_prevalence                         0
female_smokers                             15
male_smokers                               15
handwashing_facilities                   1858
hospital_beds_per_thousand                  0
life_expectancy                             0
human_development_index                     0
dtype: int64


after_vaccination.dtypes.unique()

array([dtype('O'), dtype('float64')], dtype=object)


after_vaccination.loc[after_vaccination['location']=='Thailand'].positive_rate.isnull().sum()

140


u=set()


for column in cols_to_consider:
    if(after_vaccination.loc[after_vaccination['location']==column].new_tests_per_thousand.isnull().sum()>after_vaccination.loc[after_vaccination['location']==column].shape[0]*0.9):
        u.add(column)


for column in cols_to_consider:
    if(after_vaccination.loc[after_vaccination['location']==column].handwashing_facilities.isnull().sum()>0):
        u.add(column)


for column in cols_to_consider:
    if(after_vaccination.loc[after_vaccination['location']==column].handwashing_facilities.isnull().sum()>after_vaccination.loc[after_vaccination['location']==column].handwashing_facilities.shape[0]*0.9):
        u.add(column)


len(u)

15


len(cols_to_consider)

28


final_loc_consider=cols_to_consider.intersection(u)


len(final_loc_consider)

15


after_vacc_data=after_vaccination[after_vaccination.location.isin(final_loc_consider)]
after_vacc_data.head(105390)


after_vacc_data.isnull().sum()[1:61]

continent                                   0
location                                    0
date                                        0
total_cases                                 0
new_cases                                   0
new_cases_smoothed                          0
total_deaths                                0
new_deaths                                  0
new_deaths_smoothed                         0
total_cases_per_million                     0
new_cases_per_million                       0
new_cases_smoothed_per_million              0
total_deaths_per_million                    0
new_deaths_per_million                      0
new_deaths_smoothed_per_million             0
reproduction_rate                          19
icu_patients                             1978
icu_patients_per_million                 1978
hosp_patients                            1978
hosp_patients_per_million                1978
weekly_icu_admissions                    1978
weekly_icu_admissions_per_million        1978
weekly_hosp_admissions                   1978
weekly_hosp_admissions_per_million       1978
new_tests                                 755
total_tests                               690
total_tests_per_thousand                  690
new_tests_per_thousand                    755
new_tests_smoothed                        568
new_tests_smoothed_per_thousand           568
positive_rate                             568
tests_per_case                            568
tests_units                               566
total_vaccinations                          0
people_vaccinated                         255
people_fully_vaccinated                   473
total_boosters                           1964
new_vaccinations                          217
new_vaccinations_smoothed                  15
total_vaccinations_per_hundred              0
people_vaccinated_per_hundred             255
people_fully_vaccinated_per_hundred       473
total_boosters_per_hundred               1964
new_vaccinations_smoothed_per_million      15
stringency_index                          162
population                                  0
population_density                          0
median_age                                  0
aged_65_older                               0
aged_70_older                               0
gdp_per_capita                              0
extreme_poverty                            66
cardiovasc_death_rate                       0
diabetes_prevalence                         0
female_smokers                             15
male_smokers                               15
handwashing_facilities                   1858
hospital_beds_per_thousand                  0
life_expectancy                             0
human_development_index                     0
dtype: int64


after_vacc_data.shape

(1978, 62)


#These are columns which are completely empty
after_vacc_data.drop(['icu_patients','icu_patients_per_million','hosp_patients','hosp_patients_per_million' ,'weekly_icu_admissions' ,'weekly_icu_admissions_per_million','weekly_hosp_admissions_per_million'],axis=1,inplace=True)

C:\Users\verma\anaconda3\lib\site-packages\pandas\core\frame.py:4308: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  return super().drop(


after_vacc_data.drop('weekly_hosp_admissions',axis=1,inplace=True)


after_vacc_data.drop('total_boosters',axis=1,inplace=True)


after_vacc_data.drop('total_boosters_per_hundred',axis=1,inplace=True)


after_vacc_data['handwashing_facilities'].isnull().sum()

1858


after_vacc_data.location.unique()

array(['Albania', 'Benin', 'Brazil', 'Chile', 'China', 'Egypt', 'Georgia',
       'Malaysia', 'Oman', 'Panama', 'Sri Lanka', 'Tajikistan', 'Turkey',
       'Ukraine', 'Uruguay'], dtype=object)


for cols in after_vacc_data.location.unique():
    print(cols," ",after_vacc_data.loc[after_vacc_data.location==cols].handwashing_facilities.isnull().sum()," ",after_vacc_data.loc[after_vacc_data.location==cols].handwashing_facilities.shape[0])

Albania   138   138
Benin   0   8
Brazil   184   184
Chile   234   234
China   160   160
Egypt   0   31
Georgia   98   98
Malaysia   174   174
Oman   0   66
Panama   161   161
Sri Lanka   161   161
Tajikistan   0   15
Turkey   215   215
Ukraine   162   162
Uruguay   171   171


after_vacc_data.drop('handwashing_facilities',axis=1,inplace=True)
after_vacc_data.drop('excess_mortality',axis=1,inplace=True)


after_vacc_data.isnull().sum()

iso_code                                   0
continent                                  0
location                                   0
date                                       0
total_cases                                0
new_cases                                  0
new_cases_smoothed                         0
total_deaths                               0
new_deaths                                 0
new_deaths_smoothed                        0
total_cases_per_million                    0
new_cases_per_million                      0
new_cases_smoothed_per_million             0
total_deaths_per_million                   0
new_deaths_per_million                     0
new_deaths_smoothed_per_million            0
reproduction_rate                         19
new_tests                                755
total_tests                              690
total_tests_per_thousand                 690
new_tests_per_thousand                   755
new_tests_smoothed                       568
new_tests_smoothed_per_thousand          568
positive_rate                            568
tests_per_case                           568
tests_units                              566
total_vaccinations                         0
people_vaccinated                        255
people_fully_vaccinated                  473
new_vaccinations                         217
new_vaccinations_smoothed                 15
total_vaccinations_per_hundred             0
people_vaccinated_per_hundred            255
people_fully_vaccinated_per_hundred      473
new_vaccinations_smoothed_per_million     15
stringency_index                         162
population                                 0
population_density                         0
median_age                                 0
aged_65_older                              0
aged_70_older                              0
gdp_per_capita                             0
extreme_poverty                           66
cardiovasc_death_rate                      0
diabetes_prevalence                        0
female_smokers                            15
male_smokers                              15
hospital_beds_per_thousand                 0
life_expectancy                            0
human_development_index                    0
dtype: int64


for cols in after_vacc_data.location.unique():
    print(cols," ",after_vacc_data.loc[after_vacc_data.location==cols].new_tests_smoothed_per_thousand.isnull().sum()," ",after_vacc_data.loc[after_vacc_data.location==cols].new_tests_smoothed_per_thousand.shape[0])

Albania   45   138
Benin   8   8
Brazil   184   184
Chile   0   234
China   160   160
Egypt   31   31
Georgia   9   98
Malaysia   0   174
Oman   66   66
Panama   2   161
Sri Lanka   2   161
Tajikistan   15   15
Turkey   44   215
Ukraine   0   162
Uruguay   2   171


y=set()


for cols in after_vacc_data.location.unique():
    if(after_vacc_data.loc[after_vacc_data.location==cols].new_tests_smoothed_per_thousand .isnull().sum()<after_vacc_data.loc[after_vacc_data.location==cols].new_tests_smoothed_per_thousand .shape[0]*0.8):
        print(cols," ",after_vacc_data.loc[after_vacc_data.location==cols].new_tests_smoothed_per_thousand .isnull().sum()," ",after_vacc_data.loc[after_vacc_data.location==cols].new_tests_smoothed_per_thousand .shape[0])
        y.add(cols)

Albania   45   138
Chile   0   234
Georgia   9   98
Malaysia   0   174
Panama   2   161
Sri Lanka   2   161
Turkey   44   215
Ukraine   0   162
Uruguay   2   171


final_loc_consider=cols_to_consider.intersection(y)


final_loc_consider

{'Albania',
 'Chile',
 'Georgia',
 'Malaysia',
 'Panama',
 'Sri Lanka',
 'Turkey',
 'Ukraine',
 'Uruguay'}


after_vacc_data_new=after_vacc_data


after_vacc_data_new=after_vacc_data[after_vacc_data.location.isin(final_loc_consider)]
after_vacc_data_new


after_vacc_data_new.isnull().sum()[1:61]

continent                                  0
location                                   0
date                                       0
total_cases                                0
new_cases                                  0
new_cases_smoothed                         0
total_deaths                               0
new_deaths                                 0
new_deaths_smoothed                        0
total_cases_per_million                    0
new_cases_per_million                      0
new_cases_smoothed_per_million             0
total_deaths_per_million                   0
new_deaths_per_million                     0
new_deaths_smoothed_per_million            0
reproduction_rate                         13
new_tests                                291
total_tests                              226
total_tests_per_thousand                 226
new_tests_per_thousand                   291
new_tests_smoothed                       104
new_tests_smoothed_per_thousand          104
positive_rate                            104
tests_per_case                           104
tests_units                              102
total_vaccinations                         0
people_vaccinated                         94
people_fully_vaccinated                  266
new_vaccinations                         113
new_vaccinations_smoothed                  9
total_vaccinations_per_hundred             0
people_vaccinated_per_hundred             94
people_fully_vaccinated_per_hundred      266
new_vaccinations_smoothed_per_million      9
stringency_index                         105
population                                 0
population_density                         0
median_age                                 0
aged_65_older                              0
aged_70_older                              0
gdp_per_capita                             0
extreme_poverty                            0
cardiovasc_death_rate                      0
diabetes_prevalence                        0
female_smokers                             0
male_smokers                               0
hospital_beds_per_thousand                 0
life_expectancy                            0
human_development_index                    0
dtype: int64


#Filling the missing values in new_tests_smoothed_per_thousand
for cols in after_vacc_data_new.location.unique():
    if(after_vacc_data_new.loc[after_vacc_data_new.location==cols].new_tests_smoothed_per_thousand .isnull().sum()>0):
        after_vacc_data_new.new_tests_smoothed_per_thousand.fillna(after_vacc_data_new.loc[after_vacc_data_new.location==cols].new_tests_smoothed_per_thousand.mean(),inplace=True)

C:\Users\verma\anaconda3\lib\site-packages\pandas\core\series.py:4463: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  return super().fillna(


after_vacc_data_new.drop(['new_tests','total_tests','new_tests_smoothed','total_tests_per_thousand'],axis=1,inplace=True)

C:\Users\verma\anaconda3\lib\site-packages\pandas\core\frame.py:4308: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  return super().drop(


after_vacc_data_new.drop(['new_tests_per_thousand'],axis=1,inplace=True)


for cols in after_vacc_data_new.location.unique():
    if(after_vacc_data_new.loc[after_vacc_data_new.location==cols].positive_rate .isnull().sum()<after_vacc_data_new.loc[after_vacc_data_new.location==cols].positive_rate.shape[0]*0.8):
        print(cols," ",after_vacc_data_new.loc[after_vacc_data_new.location==cols].positive_rate .isnull().sum()," ",after_vacc_data_new.loc[after_vacc_data_new.location==cols].positive_rate.shape[0])
        y.add(cols)

Albania   45   138
Chile   0   234
Georgia   9   98
Malaysia   0   174
Panama   2   161
Sri Lanka   2   161
Turkey   44   215
Ukraine   0   162
Uruguay   2   171


after_vacc_data_new.tests_units.mode()

0    tests performed
dtype: object


#Filling the missing values in positive_rate
for cols in after_vacc_data_new.location.unique():
    if(after_vacc_data_new.loc[after_vacc_data_new.location==cols].positive_rate.isnull().sum()>0):
        after_vacc_data_new.positive_rate.fillna(after_vacc_data_new.loc[after_vacc_data_new.location==cols].positive_rate.mean(),inplace=True)


#Filling the missing values in tests_per_case
for cols in after_vacc_data_new.location.unique():
    if(after_vacc_data_new.loc[after_vacc_data_new.location==cols].tests_per_case.isnull().sum()>0):
        after_vacc_data_new.tests_per_case.fillna(after_vacc_data_new.loc[after_vacc_data_new.location==cols].tests_per_case.mean(),inplace=True)


#Filling the missing values in tests_units with mode
for cols in after_vacc_data_new.location.unique():
    if(after_vacc_data_new.loc[after_vacc_data_new.location==cols].tests_units.isnull().sum()>0):
        after_vacc_data_new.tests_units.fillna(after_vacc_data_new.loc[after_vacc_data_new.location==cols].tests_units.mode()[0],axis=0,inplace=True)


#Filling the missing values in total_vaccinations
for cols in after_vacc_data_new.location.unique():
    if(after_vacc_data_new.loc[after_vacc_data_new.location==cols].total_vaccinations.isnull().sum()>0):
        after_vacc_data_new.total_vaccinations.fillna(after_vacc_data_new.loc[after_vacc_data_new.location==cols].total_vaccinations.mean(),inplace=True)


#Filling the missing values in people_vaccinated
for cols in after_vacc_data_new.location.unique():
    if(after_vacc_data_new.loc[after_vacc_data_new.location==cols].people_vaccinated.isnull().sum()>0):
        after_vacc_data_new.people_vaccinated.fillna(after_vacc_data_new.loc[after_vacc_data_new.location==cols].people_vaccinated.mean(),inplace=True)


#Filling the missing values in reproduction_rate
for cols in after_vacc_data_new.location.unique():
    if(after_vacc_data_new.loc[after_vacc_data_new.location==cols].reproduction_rate.isnull().sum()>0):
        after_vacc_data_new.reproduction_rate.fillna(after_vacc_data_new.loc[after_vacc_data_new.location==cols].reproduction_rate.mean(),inplace=True)


#Filling the missing values in people_fully_vaccinated
for cols in after_vacc_data_new.location.unique():
    if(after_vacc_data_new.loc[after_vacc_data_new.location==cols].people_fully_vaccinated.isnull().sum()>0):
        after_vacc_data_new.people_fully_vaccinated.fillna(after_vacc_data_new.loc[after_vacc_data_new.location==cols].people_fully_vaccinated.median(),inplace=True)


#Filling the missing values in people_fully_vaccinated
for cols in after_vacc_data_new.location.unique():
    if(after_vacc_data_new.loc[after_vacc_data_new.location==cols].people_vaccinated_per_hundred.isnull().sum()>0):
        after_vacc_data_new.people_vaccinated_per_hundred.fillna(after_vacc_data_new.loc[after_vacc_data_new.location==cols].people_vaccinated_per_hundred.mean(),inplace=True)


#Filling the missing values in new_vaccinations
for cols in after_vacc_data_new.location.unique():
    if(after_vacc_data_new.loc[after_vacc_data_new.location==cols].new_vaccinations.isnull().sum()>0):
        after_vacc_data_new.new_vaccinations.fillna(after_vacc_data_new.loc[after_vacc_data_new.location==cols].new_vaccinations.mean(),inplace=True)


#Filling the missing values in new_vaccinations
for cols in after_vacc_data_new.location.unique():
    if(after_vacc_data_new.loc[after_vacc_data_new.location==cols].new_vaccinations.isnull().sum()>0):
        after_vacc_data_new.new_vaccinations.fillna(after_vacc_data_new.loc[after_vacc_data_new.location==cols].new_vaccinations.mean(),inplace=True)


#Filling the missing values in people_fully_vaccinated_per_hundred
for cols in after_vacc_data_new.location.unique():
    if(after_vacc_data_new.loc[after_vacc_data_new.location==cols].people_fully_vaccinated_per_hundred.isnull().sum()>0):
        after_vacc_data_new.people_fully_vaccinated_per_hundred.fillna(after_vacc_data_new.loc[after_vacc_data_new.location==cols].people_fully_vaccinated_per_hundred.mean(),inplace=True)


#Filling the missing values in stringency_index
for cols in after_vacc_data_new.location.unique():
    if(after_vacc_data_new.loc[after_vacc_data_new.location==cols].stringency_index.isnull().sum()>0):
        after_vacc_data_new.stringency_index.fillna(after_vacc_data_new.loc[after_vacc_data_new.location==cols].stringency_index.mean(),inplace=True)


#Filling the missing values in new_vaccinations_smoothed_per_million
for cols in after_vacc_data_new.location.unique():
    if(after_vacc_data_new.loc[after_vacc_data_new.location==cols].new_vaccinations_smoothed_per_million.isnull().sum()>0):
        after_vacc_data_new.new_vaccinations_smoothed_per_million.fillna(after_vacc_data_new.loc[after_vacc_data_new.location==cols].new_vaccinations_smoothed_per_million.mean(),inplace=True)


#Filling the missing values in new_vaccinations_smoothed_per_million
for cols in after_vacc_data_new.location.unique():
    if(after_vacc_data_new.loc[after_vacc_data_new.location==cols].new_vaccinations_smoothed_per_million.isnull().sum()>0):
        after_vacc_data_new.new_vaccinations_smoothed_per_million.fillna(after_vacc_data_new.loc[after_vacc_data_new.location==cols].new_vaccinations_smoothed_per_million.mean(),inplace=True)


after_vacc_data_new.tests_units.mode()

0    tests performed
dtype: object


after_vacc_data_new.location.value_counts()

Chile        234
Turkey       215
Malaysia     174
Uruguay      171
Ukraine      162
Panama       161
Sri Lanka    161
Albania      138
Georgia       98
Name: location, dtype: int64


after_vacc_data_new.corr().new_cases.sort_values(ascending = False)

new_cases                                1.000000
new_cases_smoothed                       0.978385
new_deaths                               0.664269
new_deaths_smoothed                      0.640074
population                               0.614507
total_cases                              0.569282
people_fully_vaccinated                  0.419426
total_vaccinations                       0.417979
people_vaccinated                        0.413858
new_vaccinations_smoothed                0.405483
total_deaths                             0.375454
new_vaccinations                         0.367616
new_cases_per_million                    0.334810
new_cases_smoothed_per_million           0.323222
positive_rate                            0.320626
gdp_per_capita                           0.303927
diabetes_prevalence                      0.280356
reproduction_rate                        0.187627
male_smokers                             0.173655
stringency_index                         0.141618
human_development_index                  0.129980
female_smokers                           0.123921
new_tests_smoothed_per_thousand          0.089194
new_vaccinations_smoothed_per_million    0.086151
new_deaths_per_million                   0.082400
new_deaths_smoothed_per_million          0.053703
total_vaccinations_per_hundred           0.034819
people_vaccinated_per_hundred            0.028209
hospital_beds_per_thousand               0.005527
people_fully_vaccinated_per_hundred     -0.018206
life_expectancy                         -0.019808
population_density                      -0.027801
cardiovasc_death_rate                   -0.073286
total_cases_per_million                 -0.075454
tests_per_case                          -0.156390
median_age                              -0.198434
total_deaths_per_million                -0.224134
aged_70_older                           -0.226065
aged_65_older                           -0.249214
extreme_poverty                         -0.270962
Name: new_cases, dtype: float64


after_vacc_data_new.tests_units.value_counts()

tests performed    1340
people tested       174
Name: tests_units, dtype: int64


after_vacc_data_new.population.unique()

array([ 2877800., 19116209.,  3989175., 32365998.,  4314768., 21413250.,
       84339067., 43733759.,  3473727.])


after_vacc_data_new.hist(bins=50,figsize = (25,30))
plt.show()


after_vacc_data_new.drop(['iso_code','continent','location','date'],axis=1,inplace=True)


after_vacc_data_new.drop('tests_units',axis=1,inplace=True)


import matplotlib.pyplot as plt

from xgboost import XGBRegressor
from xgboost import XGBRFRegressor
from sklearn.model_selection import RandomizedSearchCV

from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error


from sklearn.model_selection import train_test_split


x_train,x_test,y_train,y_test=train_test_split(after_vacc_data_new.loc[:, after_vacc_data_new.columns != 'new_cases'],after_vacc_data_new.new_cases,test_size=0.2,random_state=1)


x_train.drop('new_vaccinations_smoothed',axis=1,inplace=True)
x_test.drop('new_vaccinations_smoothed',axis=1,inplace=True)


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

import seaborn as sns
import dabl


after_vacc_data_new.head()


dabl.plot(after_vacc_data_new, target_col = "new_cases")

Target looks like regression
Showing only top 10 of 38 continuous features

C:\Users\verma\anaconda3\lib\site-packages\dabl\plot\utils.py:632: UserWarning: Dropped 3 outliers in column new_vaccinations.
  warn("Dropped {} outliers in column {}.".format(
C:\Users\verma\anaconda3\lib\site-packages\pandas\io\formats\format.py:1405: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.
  for val, m in zip(values.ravel(), mask.ravel())
C:\Users\verma\anaconda3\lib\site-packages\pandas\io\formats\format.py:1405: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.
  for val, m in zip(values.ravel(), mask.ravel())
C:\Users\verma\anaconda3\lib\site-packages\dabl\plot\utils.py:374: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax.set_xticklabels(

[array([[<AxesSubplot:title={'center':'F=3.38E+04'}, xlabel='new_cases_smoothed', ylabel='new_cases'>,
         <AxesSubplot:title={'center':'F=1.19E+03'}, xlabel='new_deaths'>,
         <AxesSubplot:title={'center':'F=1.05E+03'}, xlabel='new_deaths_smoothed'>,
         <AxesSubplot:title={'center':'F=7.25E+02'}, xlabel='total_cases'>,
         <AxesSubplot:title={'center':'F=3.23E+02'}, xlabel='people_fully_vacc...'>],
        [<AxesSubplot:title={'center':'F=3.20E+02'}, xlabel='total_vaccinations', ylabel='new_cases'>,
         <AxesSubplot:title={'center':'F=3.12E+02'}, xlabel='people_vaccinated'>,
         <AxesSubplot:title={'center':'F=2.97E+02'}, xlabel='new_vaccinations_...'>,
         <AxesSubplot:title={'center':'F=2.48E+02'}, xlabel='total_deaths'>,
         <AxesSubplot:title={'center':'F=2.36E+02'}, xlabel='new_vaccinations'>]],
       dtype=object),
 array([[<AxesSubplot:title={'center':'F=7.64E-01'}, xlabel='new_cases', ylabel='population'>]],
       dtype=object)]


plt.rcParams['figure.figsize'] = (18,7)
sns.boxenplot(after_vacc_data_new['new_cases'], after_vacc_data_new['population'], palette = 'Blues')
plt.title('Gender Vs Spending Score', fontsize = 20)
plt.show()

C:\Users\verma\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(


from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_squared_error
from sklearn.ensemble import IsolationForest
from sklearn import model_selection


model = IsolationForest(random_state=47)

param_grid = {'n_estimators': [1000, 1500], 
              'max_samples': [10], 
              'contamination': ['auto', 0.0001, 0.0002], 
              'max_features': [10, 15], 
              'bootstrap': [True], 
              'n_jobs': [-1]}

grid_search = model_selection.GridSearchCV(model, 
                                           param_grid,
                                           scoring="neg_mean_squared_error", 
                                           refit=True,
                                           cv=10, 
                                           return_train_score=True)
grid_search.fit(x_train, y_train)

GridSearchCV(cv=10, estimator=IsolationForest(random_state=47),
             param_grid={'bootstrap': [True],
                         'contamination': ['auto', 0.0001, 0.0002],
                         'max_features': [10, 15], 'max_samples': [10],
                         'n_estimators': [1000, 1500], 'n_jobs': [-1]},
             return_train_score=True, scoring='neg_mean_squared_error')


iforest = IsolationForest(bootstrap=True,
                          contamination=0.0001, 
                          max_features=15, 
                          max_samples=10, 
                          n_estimators=1500, 
                          n_jobs=-1,
                         random_state=1)
y_pred = iforest.fit_predict(x_train)


X_train_iforest, y_train_iforest = x_train.loc[(y_pred != -1), :], y_train.loc[(y_pred != -1)]


def hyperParameterTuning(X_train, y_train):
    param_tuning = {
        'learning_rate': [0.01, 0.1],
        'max_depth': [3, 5, 7, 10],
        'min_child_weight': [1, 3, 5],
        'subsample': [0.5, 0.7],
        'colsample_bytree': [0.5, 0.7],
        'n_estimators' : [100, 200, 500],
        'objective': ['reg:squarederror']
    }

    xgb_model = XGBRegressor()

    gsearch = RandomizedSearchCV(estimator = xgb_model,
                           param_distributions = param_tuning,                        
                           #scoring = 'neg_mean_absolute_error', #MAE
                           scoring = 'neg_mean_squared_error',  #MSE
                            n_iter=5,
                           cv = 3,
                           n_jobs = -1,
                                 random_state=1
                           )

    gsearch.fit(X_train,y_train)

    return gsearch.best_params_
#Run only in the first run of the kernel.


best_params=hyperParameterTuning(X_train_iforest,y_train_iforest)


best_params

{'subsample': 0.7,
 'objective': 'reg:squarederror',
 'n_estimators': 500,
 'min_child_weight': 5,
 'max_depth': 10,
 'learning_rate': 0.1,
 'colsample_bytree': 0.7}


{'subsample': 0.7,
 'objective': 'reg:squarederror',
 'n_estimators': 500,
 'min_child_weight': 1,
 'max_depth': 7,
 'learning_rate': 0.01,
 'colsample_bytree': 0.7}

{'subsample': 0.7,
 'objective': 'reg:squarederror',
 'n_estimators': 500,
 'min_child_weight': 1,
 'max_depth': 7,
 'learning_rate': 0.01,
 'colsample_bytree': 0.7}


xgb_model = XGBRegressor(subsample=0.7,
 objective= 'reg:squarederror',
 n_estimators= 500,
 min_child_weight= 1,
 max_depth= 7,
 learning_rate= 0.01,
 colsample_bytree= 0.7)


xgb_model.fit(X_train_iforest,y_train_iforest)

XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
             colsample_bynode=1, colsample_bytree=0.7, enable_categorical=False,
             gamma=0, gpu_id=-1, importance_type=None,
             interaction_constraints='', learning_rate=0.01, max_delta_step=0,
             max_depth=7, min_child_weight=1, missing=nan,
             monotone_constraints='()', n_estimators=500, n_jobs=12,
             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,
             reg_lambda=1, scale_pos_weight=1, subsample=0.7,
             tree_method='exact', validate_parameters=1, verbosity=None)


y_pred=xgb_model.predict(x_test)


from sklearn.metrics import mean_squared_error


score=mean_squared_error(y_test,y_pred)
score

1094935.5229413288


plt.scatter(x_test['new_vaccinations_smoothed_per_million'] * 0.092903, y_test,color='blue', label='Real',    alpha=0.5)
plt.scatter(x_test['new_vaccinations_smoothed_per_million'] * 0.092903, y_pred,  color='red' , label='Predict', alpha=0.5)
plt.title("Real vs Predict")
plt.legend(loc='best')
plt.show()


plt.scatter(x_test['tests_per_case'] * 0.092903, y_test,          color='blue', label='Real',    alpha=0.5)
plt.scatter(x_test['tests_per_case'] * 0.092903, y_pred,  color='red' , label='Predict', alpha=0.3)
plt.title("Real vs Predict")
plt.legend(loc='best')
plt.show()


from sklearn.svm import SVR
regressor = SVR(kernel = 'sigmoid')
regressor.fit(X_train_iforest, y_train_iforest)

SVR(kernel='sigmoid')


y_pred1=regressor.predict(x_test)
score_svm=mean_squared_error(y_test,y_pred1)


score

1094935.5229413288


plt.scatter(x_test['new_vaccinations_smoothed_per_million'] * 0.092903, y_test,color='blue', label='Real',    alpha=0.5)
plt.scatter(x_test['new_vaccinations_smoothed_per_million'] * 0.092903, y_pred1,  color='red' , label='Predict', alpha=0.4)
plt.title("Real vs Predict")
plt.legend(loc='best')
plt.show()


plt.scatter(x_test['tests_per_case'] * 0.092903, y_test,          color='blue', label='Real',    alpha=0.5)
plt.scatter(x_test['tests_per_case'] * 0.092903, y_pred,  color='red' , label='Predict', alpha=0.3)
plt.title("Real vs Predict")
plt.legend(loc='best')
plt.show()


from sklearn.model_selection import RepeatedKFold
from numpy import arange
from sklearn.linear_model import Lasso


model = Lasso()
# define model evaluation method
cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
# define grid
grid = dict()
grid['alpha'] = arange(0, 1, 0.01)
# define search
search = GridSearchCV(model, grid, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)
# perform the search
results = search.fit(X_train_iforest, y_train_iforest)

C:\Users\verma\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 842525984.8280154, tolerance: 8873192.892529259
  model = cd_fast.enet_coordinate_descent(


print('MAE: %.3f' % results.best_score_)
print('Config: %s' % results.best_params_)

MAE: -650.031
Config: {'alpha': 0.99}


from sklearn.linear_model import Lasso


model = Lasso(alpha=0.99)
model.fit(X_train_iforest,y_train_iforest)

C:\Users\verma\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 842525984.8280154, tolerance: 8873192.892529259
  model = cd_fast.enet_coordinate_descent(

Lasso(alpha=0.99)


y_pred_lasso=model.predict(x_test)
score_lasso=mean_squared_error(y_pred_lasso,y_test)


score_lasso

2062358.3785437036


plt.scatter(x_test['new_vaccinations_smoothed_per_million'] * 0.092903, y_test,color='blue', label='Real',    alpha=0.5)
plt.scatter(x_test['new_vaccinations_smoothed_per_million'] * 0.092903, y_pred_lasso,  color='red' , label='Predict', alpha=0.4)
plt.title("Real vs Predict")
plt.legend(loc='best')
plt.show()


plt.scatter(x_test['tests_per_case'] * 0.092903, y_test,          color='blue', label='Real',    alpha=0.5)
plt.scatter(x_test['tests_per_case'] * 0.092903, y_pred_lasso,  color='red' , label='Predict', alpha=0.3)
plt.title("Real vs Predict")
plt.legend(loc='best')
plt.show()


from numpy import mean
from numpy import std
from sklearn.datasets import make_regression
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedKFold
from lightgbm import LGBMRegressor
# define dataset
# X, y = make_regression(n_samples=1000, n_features=20, n_informative=15, noise=0.1, random_state=7)
# define the model
model = LGBMRegressor()
# evaluate the model
cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
n_scores = cross_val_score(model, X_train_iforest, y_train_iforest, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1, error_score='raise')
# report performance
print('MAE: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))

MAE: -391.404 (84.297)


model.fit(X_train_iforest,y_train_iforest)
y_pred_lightgbm=model.predict(x_test)


score_lightgbm=mean_squared_error(y_pred_lightgbm,y_test)


score_lightgbm

1300260.2009394544


plt.scatter(x_test['new_vaccinations_smoothed_per_million'] * 0.092903, y_test,color='blue', label='Real',    alpha=0.5)
plt.scatter(x_test['new_vaccinations_smoothed_per_million'] * 0.092903, y_pred_lightgbm,  color='red' , label='Predict', alpha=0.4)
plt.title("Real vs Predict")
plt.legend(loc='best')
plt.show()


###########################


for cols in after_vacc_data.location.unique():
    if(after_vacc_data.loc[after_vacc_data.location==cols].new_tests_smoothed_per_thousand .isnull().sum()<after_vacc_data.loc[after_vacc_data.location==cols].new_tests_smoothed_per_thousand .shape[0]*0.8):
        print(cols," ",after_vacc_data.loc[after_vacc_data.location==cols].new_tests_smoothed_per_thousand .isnull().sum()," ",after_vacc_data.loc[after_vacc_data.location==cols].new_tests_smoothed_per_thousand .shape[0])

Albania   45   138
Chile   0   234
Georgia   9   98
Malaysia   0   174
Panama   2   161
Sri Lanka   2   161
Turkey   44   215
Ukraine   0   162
Uruguay   2   171


# after_vacc_data.drop(['new_tests','total_tests','new_tests_smoothed','new_tests_smoothed_per_thousand'],axis=1,inplace=True)


for col in after_vacc_data.location.unique():
    after_vacc_data.loc[after_vacc_data.location==cols].reproduction_rate=after_vacc_data.loc[after_vacc_data.location==cols].reproduction_rate.fillna(value=after_vacc_data.loc[after_vacc_data.location==cols].reproduction_rate.mean(),axis=0)

C:\Users\verma\anaconda3\lib\site-packages\pandas\core\generic.py:5494: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self[name] = value


after_vacc_data['reproduction_rate'].dtype

dtype('float64')


for cols in after_vacc_data.columns:
    if(after_vacc_data[cols].dtype=='float64' or after_vacc_data[cols].dtype=='int64' ):
        after_vacc_data[cols].fillna(after_vacc_data[cols].mean(),inplace=True)

C:\Users\verma\anaconda3\lib\site-packages\pandas\core\series.py:4463: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  return super().fillna(


after_vacc_data.tests_units.fillna('tests performed',axis=0,inplace=True)


for cols in after_vacc_data.location.unique():
    print(cols," ",after_vacc_data.loc[after_vacc_data.location==cols].tests_units.isnull().sum()," ",after_vacc_data.loc[after_vacc_data.location==cols].tests_units.shape[0])

Albania   0   138
Benin   0   8
Brazil   0   184
Chile   0   234
China   0   160
Egypt   0   31
Georgia   0   98
Malaysia   0   174
Oman   0   66
Panama   0   161
Sri Lanka   0   161
Tajikistan   0   15
Turkey   0   215
Ukraine   0   162
Uruguay   0   171


after_vacc_data.drop('date',axis=1,inplace=True)

C:\Users\verma\anaconda3\lib\site-packages\pandas\core\frame.py:4308: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  return super().drop(


after_vacc_data.location.unique()

array(['Albania', 'Benin', 'Brazil', 'Chile', 'China', 'Egypt', 'Georgia',
       'Malaysia', 'Oman', 'Panama', 'Sri Lanka', 'Tajikistan', 'Turkey',
       'Ukraine', 'Uruguay'], dtype=object)


after_vacc_data.to_csv('after_vaccination_data1.csv',index=False)


for feature in after_vacc_data:
            if after_vacc_data[feature].dtype == "float64" or "int64":
                if feature != 'new_cases':
                    fig, ax = plt.subplots()
                    ax.scatter(x = after_vacc_data[feature], y = after_vacc_data['new_cases'])
                    plt.ylabel('new_cases', fontsize=13)
                    plt.xlabel(feature, fontsize=13)
                    plt.show()


a=after_vacc_data.loc[after_vacc_data.location=='Albania'].reproduction_rate.fillna(1)
a.isnull().sum()

0


after_vacc_data.loc[after_vacc_data['location']==column].isnull().sum()>0

iso_code                                 False
continent                                False
location                                 False
total_cases                              False
new_cases                                False
new_cases_smoothed                       False
total_deaths                             False
new_deaths                               False
new_deaths_smoothed                      False
total_cases_per_million                  False
new_cases_per_million                    False
new_cases_smoothed_per_million           False
total_deaths_per_million                 False
new_deaths_per_million                   False
new_deaths_smoothed_per_million          False
reproduction_rate                        False
new_tests                                False
total_tests                              False
total_tests_per_thousand                 False
new_tests_per_thousand                   False
new_tests_smoothed                       False
new_tests_smoothed_per_thousand          False
positive_rate                            False
tests_per_case                           False
tests_units                              False
total_vaccinations                       False
people_vaccinated                        False
people_fully_vaccinated                  False
new_vaccinations                         False
new_vaccinations_smoothed                False
total_vaccinations_per_hundred           False
people_vaccinated_per_hundred            False
people_fully_vaccinated_per_hundred      False
new_vaccinations_smoothed_per_million    False
stringency_index                         False
population                               False
population_density                       False
median_age                               False
aged_65_older                            False
aged_70_older                            False
gdp_per_capita                           False
extreme_poverty                          False
cardiovasc_death_rate                    False
diabetes_prevalence                      False
female_smokers                           False
male_smokers                             False
hospital_beds_per_thousand               False
life_expectancy                          False
human_development_index                  False
dtype: bool


col=[]
for column in cols_to_consider:
    if(new_data.loc[new_data['location']==column].positive_rate.isnull().sum()>400):
        col.append(column)
        
col

['Oman',
 'Tajikistan',
 'Benin',
 'Egypt',
 'Thailand',
 'Georgia',
 'China',
 'Cambodia',
 'Brazil']


latest_cols= [x for x in cols_to_consider if not x in col or col.remove(x)]


latest_cols

['Albania',
 'South Africa',
 'Bangladesh',
 'Malaysia',
 'Turkey',
 'Colombia',
 'Paraguay',
 'Kazakhstan',
 'Panama',
 'El Salvador',
 'Uruguay',
 'Ecuador',
 'Chile',
 'Indonesia',
 'Pakistan',
 'Sri Lanka',
 'Ukraine',
 'Armenia',
 'Philippines']


for column in latest_cols:
    print(column," ",new_data.loc[new_data['location']==column].positive_rate.isnull().sum())

Albania   82
South Africa   49
Bangladesh   10
Malaysia   7
Turkey   67
Colombia   97
Paraguay   12
Kazakhstan   76
Panama   12
El Salvador   140
Uruguay   24
Ecuador   202
Chile   38
Indonesia   21
Pakistan   22
Sri Lanka   34
Ukraine   44
Armenia   19
Philippines   72


for feature in after_vacc_data:
            if after_vacc_data[feature].dtype == "float64" or "int64":
                if feature != 'new_cases':
                    fig, ax = plt.subplots()
                    ax.scatter(x = after_vacc_data[feature], y = after_vacc_data['new_cases'])
                    plt.ylabel('new_cases', fontsize=13)
                    plt.xlabel(feature, fontsize=13)
                    plt.show()

	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	new_deaths_smoothed	...	extreme_poverty	cardiovasc_death_rate	diabetes_prevalence	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality
0	AFG	Asia	Afghanistan	24-02-2020	1.0	1.0	NaN	NaN	NaN	NaN	...	NaN	597.029	9.59	NaN	NaN	37.746	0.5	64.83	0.511	NaN
1	AFG	Asia	Afghanistan	25-02-2020	1.0	0.0	NaN	NaN	NaN	NaN	...	NaN	597.029	9.59	NaN	NaN	37.746	0.5	64.83	0.511	NaN
2	AFG	Asia	Afghanistan	26-02-2020	1.0	0.0	NaN	NaN	NaN	NaN	...	NaN	597.029	9.59	NaN	NaN	37.746	0.5	64.83	0.511	NaN
3	AFG	Asia	Afghanistan	27-02-2020	1.0	0.0	NaN	NaN	NaN	NaN	...	NaN	597.029	9.59	NaN	NaN	37.746	0.5	64.83	0.511	NaN
4	AFG	Asia	Afghanistan	28-02-2020	1.0	0.0	NaN	NaN	NaN	NaN	...	NaN	597.029	9.59	NaN	NaN	37.746	0.5	64.83	0.511	NaN

	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	new_deaths_smoothed	...	extreme_poverty	cardiovasc_death_rate	diabetes_prevalence	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality
1091	ALB	Europe	Albania	25-02-2020	NaN	NaN	NaN	NaN	NaN	NaN	...	1.1	304.195	10.08	7.1	51.2	NaN	2.89	78.57	0.795	NaN
1092	ALB	Europe	Albania	26-02-2020	NaN	NaN	NaN	NaN	NaN	NaN	...	1.1	304.195	10.08	7.1	51.2	NaN	2.89	78.57	0.795	NaN
1093	ALB	Europe	Albania	27-02-2020	NaN	NaN	NaN	NaN	NaN	NaN	...	1.1	304.195	10.08	7.1	51.2	NaN	2.89	78.57	0.795	NaN
1094	ALB	Europe	Albania	28-02-2020	NaN	NaN	NaN	NaN	NaN	NaN	...	1.1	304.195	10.08	7.1	51.2	NaN	2.89	78.57	0.795	NaN
1095	ALB	Europe	Albania	29-02-2020	NaN	NaN	NaN	NaN	NaN	NaN	...	1.1	304.195	10.08	7.1	51.2	NaN	2.89	78.57	0.795	2.17
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
105386	URY	South America	Uruguay	12-08-2021	382997.0	124.0	120.286	5996.0	5.0	2.000	...	0.1	160.708	6.93	14.0	19.9	NaN	2.80	77.91	0.817	NaN
105387	URY	South America	Uruguay	13-08-2021	383101.0	104.0	115.143	5998.0	2.0	1.714	...	0.1	160.708	6.93	14.0	19.9	NaN	2.80	77.91	0.817	NaN
105388	URY	South America	Uruguay	14-08-2021	383212.0	111.0	121.714	6001.0	3.0	2.143	...	0.1	160.708	6.93	14.0	19.9	NaN	2.80	77.91	0.817	NaN
105389	URY	South America	Uruguay	15-08-2021	383292.0	80.0	112.286	6003.0	2.0	2.429	...	0.1	160.708	6.93	14.0	19.9	NaN	2.80	77.91	0.817	NaN
105390	URY	South America	Uruguay	16-08-2021	383387.0	95.0	111.429	6004.0	1.0	2.143	...	0.1	160.708	6.93	14.0	19.9	NaN	2.80	77.91	0.817	NaN

	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	new_deaths_smoothed	...	extreme_poverty	cardiovasc_death_rate	diabetes_prevalence	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality
1411	ALB	Europe	Albania	10-01-2021	63595.0	562.0	593.857	1241.0	8.0	6.857	...	1.1	304.195	10.08	7.1	51.2	NaN	2.89	78.57	0.795	NaN
1413	ALB	Europe	Albania	12-01-2021	64627.0	656.0	620.571	1252.0	5.0	6.000	...	1.1	304.195	10.08	7.1	51.2	NaN	2.89	78.57	0.795	NaN
1414	ALB	Europe	Albania	13-01-2021	65334.0	707.0	618.000	1256.0	4.0	5.571	...	1.1	304.195	10.08	7.1	51.2	NaN	2.89	78.57	0.795	NaN
1415	ALB	Europe	Albania	14-01-2021	65994.0	660.0	612.714	1261.0	5.0	5.429	...	1.1	304.195	10.08	7.1	51.2	NaN	2.89	78.57	0.795	NaN
1416	ALB	Europe	Albania	15-01-2021	66635.0	641.0	608.143	1265.0	4.0	5.000	...	1.1	304.195	10.08	7.1	51.2	NaN	2.89	78.57	0.795	NaN

	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	new_deaths_smoothed	...	extreme_poverty	cardiovasc_death_rate	diabetes_prevalence	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality
1411	ALB	Europe	Albania	10-01-2021	63595.0	562.0	593.857	1241.0	8.0	6.857	...	1.1	304.195	10.08	7.1	51.2	NaN	2.89	78.57	0.795	NaN
1413	ALB	Europe	Albania	12-01-2021	64627.0	656.0	620.571	1252.0	5.0	6.000	...	1.1	304.195	10.08	7.1	51.2	NaN	2.89	78.57	0.795	NaN
1414	ALB	Europe	Albania	13-01-2021	65334.0	707.0	618.000	1256.0	4.0	5.571	...	1.1	304.195	10.08	7.1	51.2	NaN	2.89	78.57	0.795	NaN
1415	ALB	Europe	Albania	14-01-2021	65994.0	660.0	612.714	1261.0	5.0	5.429	...	1.1	304.195	10.08	7.1	51.2	NaN	2.89	78.57	0.795	NaN
1416	ALB	Europe	Albania	15-01-2021	66635.0	641.0	608.143	1265.0	4.0	5.000	...	1.1	304.195	10.08	7.1	51.2	NaN	2.89	78.57	0.795	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
105386	URY	South America	Uruguay	12-08-2021	382997.0	124.0	120.286	5996.0	5.0	2.000	...	0.1	160.708	6.93	14.0	19.9	NaN	2.80	77.91	0.817	NaN
105387	URY	South America	Uruguay	13-08-2021	383101.0	104.0	115.143	5998.0	2.0	1.714	...	0.1	160.708	6.93	14.0	19.9	NaN	2.80	77.91	0.817	NaN
105388	URY	South America	Uruguay	14-08-2021	383212.0	111.0	121.714	6001.0	3.0	2.143	...	0.1	160.708	6.93	14.0	19.9	NaN	2.80	77.91	0.817	NaN
105389	URY	South America	Uruguay	15-08-2021	383292.0	80.0	112.286	6003.0	2.0	2.429	...	0.1	160.708	6.93	14.0	19.9	NaN	2.80	77.91	0.817	NaN
105390	URY	South America	Uruguay	16-08-2021	383387.0	95.0	111.429	6004.0	1.0	2.143	...	0.1	160.708	6.93	14.0	19.9	NaN	2.80	77.91	0.817	NaN

	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	new_deaths_smoothed	...	aged_70_older	gdp_per_capita	extreme_poverty	cardiovasc_death_rate	diabetes_prevalence	female_smokers	male_smokers	hospital_beds_per_thousand	life_expectancy	human_development_index
1411	ALB	Europe	Albania	10-01-2021	63595.0	562.0	593.857	1241.0	8.0	6.857	...	8.643	11803.431	1.1	304.195	10.08	7.1	51.2	2.89	78.57	0.795
1413	ALB	Europe	Albania	12-01-2021	64627.0	656.0	620.571	1252.0	5.0	6.000	...	8.643	11803.431	1.1	304.195	10.08	7.1	51.2	2.89	78.57	0.795
1414	ALB	Europe	Albania	13-01-2021	65334.0	707.0	618.000	1256.0	4.0	5.571	...	8.643	11803.431	1.1	304.195	10.08	7.1	51.2	2.89	78.57	0.795
1415	ALB	Europe	Albania	14-01-2021	65994.0	660.0	612.714	1261.0	5.0	5.429	...	8.643	11803.431	1.1	304.195	10.08	7.1	51.2	2.89	78.57	0.795
1416	ALB	Europe	Albania	15-01-2021	66635.0	641.0	608.143	1265.0	4.0	5.000	...	8.643	11803.431	1.1	304.195	10.08	7.1	51.2	2.89	78.57	0.795
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
105386	URY	South America	Uruguay	12-08-2021	382997.0	124.0	120.286	5996.0	5.0	2.000	...	10.361	20551.409	0.1	160.708	6.93	14.0	19.9	2.80	77.91	0.817
105387	URY	South America	Uruguay	13-08-2021	383101.0	104.0	115.143	5998.0	2.0	1.714	...	10.361	20551.409	0.1	160.708	6.93	14.0	19.9	2.80	77.91	0.817
105388	URY	South America	Uruguay	14-08-2021	383212.0	111.0	121.714	6001.0	3.0	2.143	...	10.361	20551.409	0.1	160.708	6.93	14.0	19.9	2.80	77.91	0.817
105389	URY	South America	Uruguay	15-08-2021	383292.0	80.0	112.286	6003.0	2.0	2.429	...	10.361	20551.409	0.1	160.708	6.93	14.0	19.9	2.80	77.91	0.817
105390	URY	South America	Uruguay	16-08-2021	383387.0	95.0	111.429	6004.0	1.0	2.143	...	10.361	20551.409	0.1	160.708	6.93	14.0	19.9	2.80	77.91	0.817

Outlier removal using isolation forest algorithm-¶

XGBoost algorithm-¶

Support vector machine¶

Lasso Regression implementation-¶

LightGBM¶